A couple of months ago, I began a new job as a Production Manager for a local flavored syrup manufacturer. Having been a production manager in the past, I knew that one of my main goals would be to get the most out of each production line on a daily basis. Accepting this new position also meant that I would once again be driving forty or so minutes to and from work each day. As a person who takes how he spends his personal time seriously, I immediately began looking for ways to reduce the time I was going to be spending in the car during my five hundred commutes each year.

I began by wondering what people who do not possess industrial engineering skills do to optimize their commute time. Do they employ the same time and system analysis tools that we IEs do, and just don't know it, or does the non-IE commuter simply take longer on average to drive the same route to work and back? It has been my observation to-date that that latter appears to be more the case – industrial engineers have a competitive advantage when it comes to being an effective commuter.

More than anything else, we IEs understand that we are throwing ourselves at the mercy of the traffic system to a large degree. We understand that our success in getting to and from work effectively is based on the way the roads are designed, where the traffic signals are placed, and when we choose to leave our home or the plant. This understanding means, I believe, that we are less likely than the average drive to become stressed out over possibly being late, being caught in bad traffic, or not making that last light. Is it possible that the rate of road rage incidents is closely correlated with the percentage of industrial engineers on the road in a given geographic area?

Industrial engineers also understand how systems improvement is supposed to work. We can chart (at least mentally) our actual travel times each day, along with the key variables that affected them, and identify which transportation system features affect our average commute times the most. We can also predict with pretty good accuracy what the odds of being ten percent faster or slower than average will be, since we understand how variation and standard deviation work. Finally, we can experiment with different travel options, compare the results in a fact-based manner, and define the route that will be give us the optimum travel time most often.

As a Production Manager, I am charged with getting the most out of my production lines, just as I am personally responsible for improving the process that I use to get to and from work each day. In turn, I should expect the use of data and systems analysis to yield similar results on the job. Just as it is unreasonable to expect myself to reduce my average commute time by ten percent without first making some form of fundamental system change, I should not expect the production line to magically become ten percent more efficient simply because the request or demand was made.

I draw this analogy for a very simple reason. We are fast approaching that time of the year when we establish performance goals, identify key projects, and develop our operating budgets for the coming year. More often than not, we tend to set goals by taking this year's performance average and bumping it up by five to ten percent. It is a more rare case when we first define the high leverage changes we need to make, and then project the impact that these changes will have on system performance. It is more often the case that we agree to a percentage that seems a little outrageous and yet somehow, we still find ways to attain it.

I would like to see us stop using such a practice for setting goals. We should work to understand our systems before attempting to define what they are capable of. Why does it take me thirty-five minutes on average to drive to and from work? Why can I sometimes make the trip in twenty-five minutes, while on other days it can take as long as fifty minutes? What does the ‘average travel time' histogram look like? Given the system factors that I control and change, how fast can I go on average?

We would not expect a professional hockey team to average ten goals a game under the current design of the hockey “system.” We would also not expect ourselves to be able to reduce our average commute time by ten percent in the coming year, simply because ten is a nice round number (and it is slightly higher than the goal that our neighbor Mr. Jones agreed to). That said, why should we expect our plant operating costs to be ten percent lower or our cycle times to be ten percent faster, unless we are fundamentally changing the system in several ways to help reach these goals consistently?

All too often we rely on exhortations, banners, and even pay incentives to drive us to higher levels of performance in our organizations, when instead we should be working much harder to better understand our key work systems and to define those key system changes that will impact performance the most. You can attempt to entice me to get to work more quickly by saying that you will pay me ten dollars whenever I make the drive in twenty minutes or less, but I know from experience that the odds of my collecting much of that money are very limited. Systems only give us what they are designed to give us. In order to make any money, I would have to make 23 out of the 28 lights and break a speeding law or two.

As you begin the process of setting performance goals for the coming year, try to first look at what your key systems have been giving you in the past performance wise, and why. Try to identify through data based analysis and front line input which system changes offer the best potential for improved performance. Use this analysis to help you set realistic performance improvement goals that are contingent on these systems changes being implemented. Watch how the data changes after the improvements are put in place, and use this feedback to assess the effectiveness of the change.

I hope that you can do your best to avoid the seemingly irresistible urge to shortcut the goal setting process by merely bumping the current average by a percentage. If you can instead look to continually improve systems performance through analysis and dialogue, the goal setting process will become one that is based more on data based projection and prediction rather than pain, guessing, and pleading. Keep improving!

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